From Murmann Mixed-Signal Group

Instrumenting the Nervous System at Single-Cell Resolution.

The goal of my research is to design the next generation of neural interfaces that allow single-cell resolution when communicating with the nervous system. Specifically, I will design a high-resolution large-scale neural interface for an artificial retina. An artificial retina is a device that replaces the function of retinal circuitry lost to disease, [1], [2]. In principle, a device able to reproduce the natural pattern of activation of the ganglion cells in the retina, using electrical stimulation, and transmitting this neural signal to the brain, would restore vision in the patient. The overall system is depicted in Fig. 1 - the recorded neural data will be analyzed to create a dictionary of the available neurons (cells we can record from) and the accessible neurons (cells we can stimulate with single-cell resolution). Because it is relatively well understood and easily accessible, the retina is an ideal system to develop such a device.

The main obstacle is that there are many types of cells in the retina that deliver distinct visual signals to different targets in the brain, thus, the distinct cell types must be addressed independently to re-create the neural code. Because the different cell types are intermixed in the neural circuitry of the retina, this requires cellular resolution interfaces, with high channel count to recreate complex patterns in the neural network. Both resolution and channel count come at the cost of power and area consumption, resources heavily limited in implantable devices.A challenge that neural interfaces face is the massive amount of data that they generate. 10,000 channels with 10 bit resolution at 20,000 samples per second generate 2 Gbps of neural data. This would require 125 times the data rate used to stream 4K videos from Netflix - 90% of which contains no information, since the spike rate of neurons is much less than the sampling frequency. The energy cost of generating and transferring this large amount of data is prohibitive. Without a radical change in the way neural interfaces are designed, the power dissipation will exceed the target for clinically viable devices by more than an order of magnitude. Therefore, an efficient device must learn the underlying biological structure, dynamically adapt and redistribute its resources to maximize the amount of information that can be extracted from any given channel. We propose a novel competitive multiplexing technique that we recently discovered, that shows high promise for solving the data explosion problem by exploiting the structure of neural signals. Here, only channels recording a spike will get access to a limited number of readout lines. This exciting new architecture is granting exceptional preliminary results in simulation, and we believe that developing this device into a functioning chip may revolutionize the way neural interfaces are designed.The ultimate goal of this project is to design a neural interface that can interact with single cell resolution to the retina, within the power and area constraints of an implantable device. We aim for this technology to effectively translate to brain applications.